The VISION that builds the bridge

The line of thought across product design and forecasting has a similar call for “clarity” and “thoroughness” in vision. A product design that does not consider the target customer and target product and a forecasting model that does not consider the inherent bias and assumptions are bound to pose unforeseen challenges and bottlenecks during plan execution.

Innovation is a key driver that could be used to break conventional supply chain costs. The cases of automotive engineering innovation by Gordon Murray and Tata Nano strongly reflect the same. It helps improve the operational efficiency which in turn improves the product quantity at the same or a much lower cost. It not only gives an optimistic forecast but also facilitates enhanced product designing. But at the same time the benefits of innovation or that of other nuances like iterative designing or expertise distribution will have a relevant context only based upon the broader vision that an organization might strive for. The former are just the means to achieve the end goal.

Making the delivery end of the supply chain localized and customized helps deliver the unique customer experience. This further helps market players to be competent in their product pricing as a number of loose ends in the supply chain cycle could be identified by efficient feedback loops. IKEA, with its Stint tested “assemble at home” strategy and Herman Miller’s cradle-to-cradle designing reinforce these facts along with the combination of creating a sense of distinguished credibility and goodwill across its end customer segment. Such conscious efforts from an organization to reduce final pricing and to increase recyclable constituent composition create positive vibes and fair competition within any industry. This favor is returned to the generating organization in terms of non-linear growth above the industry average. This is an incentive for being pioneers in envisioning unconventional methodologies to shake up the status quo, thereby promoting creativity in an otherwise flat buy and sell market.  These pioneers are the ones that make the ends (product design and corresponding forecast) meet by setting the appropriate context.

How is the effect of product designing and supply chain like those discussed in the reading quantified to account for the accuracy of the forecasting models? In the search for continuously improving these forecast models how is the technology industry coping with the ever shrinking supply chain cycles?

Below are some of the additional readings and the references:

References & Readings:

  http://ip2.foresightst.com/IPPipeline/Licensing%20Fact%20Sheets/Wu%20et%20al%20Intel%20Implementation%20Paper.pdf
http://dspace.mit.edu/bitstream/handle/1721.1/39816/ESD-260JFall2003/NR/rdonlyres/Engineering-Systems-Division/ESD-260JFall2003/08F26A35-E698-4FC6-8AC6-1B7445F1CE04/0/l2_3demfcastpmas.pdf
http://www.theengineer.co.uk/in-depth/interviews/automotive-engineer-gordon-murray/1010726.article
http://karlulrich.pbworks.com/f/IKEA-cost-article.pdf
http://www.businessweek.com/stories/2008-02-27/learning-from-tatas-nanobusinessweek-business-news-stock-market-and-financial-advice
http://www.c2cproducts.com/images/JIndustrialEcology_Herman_Miller_17mar06.pdf
http://www.strategy-business.com/article/10202?pg=5

     
      Manoj Ravi